This Digital Image Processing and Computer Vision Fundamentals course introduces the core concepts behind how digital images are created, processed, and analyzed by computer systems. It begins with an overview of the field and its historical development, giving learners a strong conceptual foundation.
The course explains how images are formed, represented, and sampled, which is essential for understanding how real-world scenes are converted into digital data. Learners are introduced to linear filtering techniques used to enhance or modify images, as well as the frequency domain representation that helps analyze image information from a signal-processing perspective.
Key topics such as edge detection are covered in detail, showing how systems identify object boundaries and important structural features in images. The course also explores advanced concepts like blobs, corners, and feature extraction, which are widely used in object recognition and tracking applications.
Additionally, learners study scale space theory, image pyramids, and filter banks, which are important for handling images at multiple resolutions and detecting features across different scales.
By the end of this course, students will understand the mathematical and practical foundations of computer vision and be able to apply essential image processing techniques used in AI, robotics, and visual recognition systems.
